Regional-Scale Permafrost Mapping by Scalable Machine Learning
Bibliographic record
Abstract
This thesis presents an integrated approach to mapping permafrost at high-resolution using scalable machine learning (ML) methods and is composed of two interrelated components. The first component is a research paper outlining the development and validation of a ML framework and model to predict permafrost presence and ice content across west‐central Yukon, Canada (Chapter 2). The second component is a published, open-source R package developed in support of the aforementioned permafrost prediction model (Appendix), created with the goal of facilitating future applications of ML techniques to spatial predictions of permafrost or other spatial phenomena with built in support for model optimization. In Chapter 2, permafrost distribution and relative ice content is modelled across west-central Yukon using as a training set a detailed field map of permafrost conditions across a proposed mine site (the Coffee Gold Project). Permafrost distribution was constructed using ML and a series of spatial predictors derived primarily from an open-access Canadian high-resolution (16 m) digital elevation model (e.g., elevation, slope, aspect, curvature, solar radiation). Using random forest (RF) classification, we predicted permafrost distribution across the training area with a balanced accuracy of approximately 0.86. We also validated the model's performance by accurately reproducing a permafrost distribution map produced at a second, 35 km distant mining project (Casino Mine Project) to a 73 % agreement. This high accuracy underscores the potential for ML to bridge the scale gap between detailed local field observations and broader regional permafrost mapping efforts and substantially improves upon the previously existing permafrost models over Yukon in terms of resolution and accuracy. Complementing the research paper, the SAiVE R package (Appendix) offers a robust, reproducible, and user-friendly tool to streamline the creation of optimized ML models predicting spatially dependent variables. The package streamlines tasks ranging from predictor variable extraction and spatial sampling, model training, testing, and comparison and selection, and (if desired) the extrapolation of predictions over extensive geographic areas. By integrating functionalities for hyperparameter tuning and variable importance ranking, SAiVE not only facilitates advanced spatial modelling for permafrost studies but also provides a flexible framework adaptable to other environmental applications. Together, these contributions offer a scalable solution for permafrost mapping that supports environmental management, infrastructure planning, and climate adaptation strategies in rapidly changing Arctic and sub-Arctic regions. I expect that the modelling approach presented here will lead to improved representations of permafrost in Yukon and beyond, with direct applications for research, hydrology, mining exploration, and infrastructure projects looking to extrapolate local permafrost measurements to broader scales.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.030 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".